A Comparative Analysis of EO-1 Hyperion, Quickbird and Landsat TM Imagery for Fuel Type Mapping of a Typical Mediterranean Landscape
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Remote Sens. 2014, 6, 1684-1704; doi:10.3390/rs6021684 OPEN ACCESS remote sensing ISSN 2072-4292 www.mdpi.com/journal/remotesensing Article A Comparative Analysis of EO-1 Hyperion, Quickbird and Landsat TM Imagery for Fuel Type Mapping of a Typical Mediterranean Landscape Giorgos Mallinis 1,*, Georgia Galidaki 2 and Ioannis Gitas 2 1 Department of Forestry and Management of the Environment and Natural Resources, School of Agricultural Sciences and Forestry, Democritus University of Thrace, Orestiada 68200, Greece 2 Forestry and Natural Environment, School of Agriculture, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece; E-Mails: [email protected] (G.G.); [email protected] (I.G.) * Author to whom correspondence should be addressed; E-Mail: [email protected]; Tel.: +30-2552-041-107; Fax: +30-2552-041-192. Received: 31 December 2013; in revised form: 8 February 2014 / Accepted: 13 February 2014 / Published: 20 February 2014 Abstract: Forest fires constitute a natural disturbance factor and an agent of environmental change with local to global impacts on Earth’s processes and functions. Accurate knowledge of forest fuel extent and properties can be an effective component for assessing the impacts of possible future wildfires on ecosystem services. Our study aims to evaluate and compare the spectral and spatial information inherent in the EO-1 Hyperion, Quickbird and Landsat TM imagery. The analysis was based on a support vector machine classification approach in order to discriminate and map Mediterranean fuel types. The fuel classification scheme followed a site-specific fuel model within the study area, which is suitable for fire behavior prediction and spatial simulation. The overall accuracy of the Quickbird-based fuel type mapping was higher than 74% with a quantity disagreement of 9% and an allocation disagreement of 17%. Both classifications from the Hyperion and Landsat TM fuel type maps presented approximately 70% overall accuracy and 16% allocation disagreement. The McNemar’s test indicated that the overall accuracy differences between the three produced fuel type maps were not significant (p < 0.05). Based on both overall and individual higher accuracies obtained with the use of the Quickbird image, this study suggests that the high spatial resolution might be more decisive than the high spectral resolution in Mediterranean fuel type mapping. Remote Sens. 2014, 6 1685 Keywords: SVMs; high spatial resolution; high spectral resolution; fuel type mapping; fire risk; fire impact; object-based; pixel-based 1. Introduction Forest fires are an integral component of Mediterranean ecosystems since the Miocene. However, the second half of the past century presented a major change and regime shift [1]. This change in fire regime observed in recent decades in European Mediterranean [2–4] and elsewhere [5,6], is expressed by an increase of the number of fires and surface burnt [4,7,8], leading to more than 400,000 ha annually burnt areas in the EU Mediterranean region [1]. Given the spectacular increase in the number of wildland fires during the recent decades in the Euro-Mediterranean region, effective fire management strategies are needed to minimize fire hazard. Efficient forest fire management requires an accurate knowledge of fuels at many spatial and temporal scales [9]. Fuels are defined as the physical characteristics, such as loading, size, and bulk density, of the live and dead biomass that contribute to the spread, intensity, and severity of wildland fire [10,11]. Based on knowledge of the spatial extent of the fuels, national authorities and fire managers can design fire prevention, detection, suppression and fire effects assessment strategies [12], such as the use and distribution of available fire-fighting resources, fuel treatment practices, fire towers and water tanks construction, trace gas emissions, and monitoring of vegetation recovery after fire [13]. In addition, accurate knowledge of forest fuel extent can be an effective component for mitigating the impacts of future wildfire on ecosystem services and restoring desirable structural attributes to fire suppressed forests as well as to infer ecosystem impacts of historically natural wildfires [14]. Remote sensing technology is capable of produce spatial estimations of fuel types and fuel loads, based on satellite systems of different spatial, temporal and spectral characteristics [9,15]. Most of the aforementioned studies relied on the use of broadband, medium spatial resolution imagery such as the Landsat TM and SPOT series, based on pixel-based classification approaches [16–18]. The value of imagery from the Advanced Spaceborne Thermal Emission and ReflectionRadiometer (ASTER) in fuel type and properties mapping has also been evaluated in conjunction with either pixel-based [19–21] or Geographic Object Based Image Analysis (GEOBIA) approaches [22,23]. Mediterranean forest areas are known for the high spatiotemporal heterogeneity of their vegetation patterns with respect to species composition and stand structure [24–27]. While this heterogeneity makes them aesthetically attractive, it hinders accurate mapping of forest-related parameters using applied remote sensing techniques [28,29]. Arroyo et al. [30], Gitas et al. [31] and Mallinis et al. [13] evaluated the use of multiscale object-based approaches with high spatial resolution imagery, in order to explore forest fuels delineation in Mediterranean areas. Moreover Lasaponara and Lanorte [32] employed a pixel-based approach with a Quickbird imagery to delineate fuel types according to the Prometheus fuel classification scheme. However, very few studies have examined the possibility of hyperspectral data to overcome the limitations of traditional multispectral imagery [33–35]. Roberts et al. [34] evaluated data from the Earth Observing-1 (EO-1) Hyperion and AVIRIS sensors for mapping six land-cover classes in Santa Remote Sens. 2014, 6 1686 Barbara, California for fire danger assessment while Jia et al. [33] relied solely on AVIRIS data for assessing spatial patterns of forest fuel types in the Colorado Front Range. Lasaponara et al. [36] using airborne hyperspectral Multispectral Infrared and Visible Imaging Spectrometer (MIVIS) discriminated an adapted Prometheus fuel types scheme in Italy, using a conventional maximum likelihood classification. Finally, Keramitsoglou et al. [20] evaluated the use of EO-1 Hyperion satellite hyperspectral imagery near Athens, Greece for discriminating six fuel types. While several recent studies have explored differences in information extraction efficacy between high-spatial resolution, multispectral and medium-spatial resolution hyperspectral imagery [37,38], none of them has focused on fuel type mapping purposes. Hence, when extracting forest related parameters, and particularly over complex forest areas, the issue regarding the choice of the most suitable spectral and spatial resolution for classification is of key importance [39]. Besides the exploration of spatial and spectral information exploitation, the remote sensing community investigates the use of various techniques and algorithms for extraction of useful information. Support vector machines (SVMs) are particularly appealing due to their ability to generalize well even with limited training samples, a common limitation for remote sensing applications [40]. SVMs have been used in remote sensing-based estimation and monitoring of biophysical parameters such as chlorophyll concentration, gross primary productivity, evapotranspiration, land/use land cover tasks, benthic habitat mapping using multi-beam sonar, forest mapping, burned area mapping, tree species detection using a variety of data sources ranging from VHR imagery to LIDAR datasets [9,40–44]. Comparative studies have also shown that classification by SVMs can be more accurate than popular contemporary techniques such as neural networks and decision trees as well as conventional probabilistic classifiers such as the maximum likelihood classification [45–48]. The main goal of our study is to evaluate and compare the potential of original spectral and spatial information of Quickbird, Landsat TM, and EO-1 Hyperion, joined with an SVMs classification approach, in discriminating Mediterranean fuel types on the same study site. The specific objectives of this study included (1) fuel type mapping based on a Quickbird image using an object-based approach and SVMs classifier (2) generation of fuel type maps from Landsat-TM and EO-1 Hyperion imagery using a pixel-based approach and SVMs classifier and (3) assessment of the results and comparative evaluation of the potential of spatial against spectral resolution for fuel type mapping. 2. Materials and Methods 2.1. Outline of the Methodology We evaluated and compared the fuel type maps generated upon the use of three satellite images with high/medium (i.e., Quickbird), medium/high (i.e., Landsat TM) and medium/very high (i.e., EO-1 Hyperion), spatial/spectral resolution over the same Mediterranean site in northern Greece. Pre-processing of the hyperspectral imagery included removal of non-calibrated and problematic bands, and a forward/backward Minimum Noise Fraction Transformation (MNFT) in order to separate the strong component of data noise (Figure 1). Remote Sens. 2014, 6 1687 Figure 1. Overall process diagram of this research study. EO-1-Hyperion Landsat TM Quickbird Invalid band removal (non- calibrated, overlapping, water absorption, heavy vertical stripping) Atmospheric Atmospheric